Radiation therapy treatment verification with electronic portal imaging device transit images

ABSTRACT

A method for radiation therapy treatment verification includes acquiring: treatment plan information from a radiation therapy system; patient image data; and transit image data received from an electronic portal imaging device during radiation therapy. The treatment plan information is divided into a plurality of segments. Predicted segment image data is determined utilizing a predicted image calculation algorithm and at least the patient image data and the treatment plan information. A predicted integrated image is determined through superposition of the predicted segment image data. Measured segment responses are determined from the transit image data utilizing the predicted segment image data and the predicted integrated image. The measured segment responses are converted to measured segment doses. A measured dose map having a sum of the measured segment doses is compared to a planned dose map based on the treatment plan information to assess radiation treatment delivery.

CROSS-REFERENCE TO RELATED APPLICATIONS

The current application is a continuation of and claims priority under35 U.S.C. § 120 to U.S. patent application Ser. No. 15/908,538 filedFeb. 28, 2018 and entitled “RADIATION THERAPY TREATMENT VERIFICATIONWITH ELECTRONIC PORTAL IMAGING DEVICE TRANSIT IMAGES,” which claimspriority under 35 U.S.C. § 119(e) to U.S. Provisional Patent ApplicationNo. 62/465,126 filed Feb. 28, 2017 and entitled “RADIATION THERAPYTREATMENT VERIFICATION WITH ELECTRONIC PORTAL IMAGING DEVICE TRANSITIMAGES,” the contents of each are hereby incorporated by reference intheir entirety.

BACKGROUND

Radiation therapy is used to treat cancerous tumors with ionizingradiation that kills the affected cancer cells. External beamradiotherapy is one method for delivering the ionizing radiation. Insuch therapy, a patient is placed on a couch and a radiotherapy beamgenerator (for example, a linear accelerator) is positioned to directthe ionizing radiation at the patient's tumor. One method fordetermining the proper positioning of the patient with respect to thebeam is to use data from a radiation detector, for example an electronicportal imaging device (EPID). Images from an EPID depict the radiationexiting the patient, essentially providing an x-ray image that can beused to properly locate the patient with respect to the beam. Somemodern EPID devices use a phosphor and an array of photosensors todetect radiation exiting the patient. Light from the phosphor isconverted to an electrical signal and read by a computer to generate amapping of the radiation pattern striking the phosphor.

SUMMARY

In a first aspect, a method for radiation therapy treatment verificationincludes, acquiring treatment plan information from a radiation therapysystem, patient image data, and transit image data received from anelectronic portal imaging device during radiation therapy. The treatmentplan information is divided into a plurality of segments. Predictedsegment image data is determined utilizing a predicted image calculationalgorithm and at least the patient image data and the treatment planinformation. A predicted integrated image is determined throughsuperposition of the predicted segment image data. Measured segmentresponses are determined from the transit image data utilizing thepredicted segment image data and the predicted integrated image. Themeasured segment responses are converted to measured segment doses. Ameasured dose map having a sum of the measured segment doses is comparedto a planned dose map based on the treatment plan information to assessradiation treatment delivery.

In some variations, one or more of the following features can be added,in any combination.

A difference between the measured dose map and the planned dose map canbe transmitted to a recipient device when the planned dose mapcorresponds to a sum of the plurality of segments. The converting canutilize an effective field size calculator or a ray tracer algorithm.

The comparing can also include displaying, at an electronic display, areport comprising the difference or generating, at an electronic device,a warning based on the difference.

The converting can also include accessing, from at least one database, ameasurement of an output of the treatment beam, the patient image data,and a physical configuration of the radiation therapy system. Also, aconversion factor can be generated based on the accessed measurement,the patient image data, and the physical configuration corresponding toa segment.

The conversion factor can be applied to the measured segment responsesto generate the measured segment doses. A neural network can generatethe predicted integrated image by weighting a predicted segment responsecontribution as part of an input layer of the neural network.

The patient image data comprises three-dimensional images of patientanatomy. Each of the segments can correspond to a time window where thedelivery of dose to a portion of a patient anatomy is substantiallyconstant. The segments can include at least one segment corresponding toless than 1 second of a treatment plan.

The comparison of measured segment doses to the desired doses comprisesthe comparison of a first sum of the measured segment doses to a secondsum of the desired doses.

An electronic warning can be generated at a display device when thecomparison of a first sum of measured segment doses to a second sum ofthe desired doses is outside of a predetermined dose limit.

Determination of the measured segment responses can also includeextracting a predicted response contribution based on the predictedsegment image data and the predicted integrated image. The measuredsegment response can be generated from the predicted responsecontribution and the transit data.

In an interrelated aspect, a method generates, at a server, a compositecalibration image by operations including applying a first weight to afirst calibration image from an electronic portal imaging device togenerate a weighted first calibration image. A second weight is appliedto a second calibration image from the electronic portal imaging deviceto generate a weighted second calibration image. The weighted firstcalibration image and the weighted second calibration image aresuperimposed to generate the composite calibration image. A dosecalculation engine generates a composite dose map based on a first dosemap, a second dose map, the first weight, and the second weight. At theserver, a conversion factor is generated that converts images to dosemaps, where the conversion factor corresponds to a first radiationtherapy system configuration that is different than a second radiationtherapy system configuration corresponding to at least one of the firstcalibration image or the second calibration image. The conversion factoris stored in a multidimensional conversion structure. The conversionfactor generated from the composite dose map and the compositecalibration image is transmitted from the server to a requesting device.

In some variations, one or more of the following features can be added,in any combination.

The conversion factor in the multidimensional conversion structure canbe based on the composite calibration image and the composite dose map.The conversion factor can be generated by the superposition of more thantwo calibration images and more than two dose maps.

The conversion factors in the multidimensional conversion structure canbe based on a plurality of basis parameters comprising at least one ofan effective field size, a radiological path length, an exit distance, apixel position, and a primary signal ratio.

The method can also include populating the multidimensional conversionstructure with additional conversion factors corresponding to a range ofprimary signal ratios. The first weight can be a primary radiationfraction, the second weight can be a secondary radiation fraction, andthe primary signal ratio can be based on the primary radiation fractionand the secondary radiation fraction.

The composite dose map can be generated by operations includinggenerating a first dose map based on the first calibration image and afirst conversion factor in the multidimensional conversion structure. Asecond dose map can be generated based on the second calibration imageand a second conversion factor in the multidimensional conversionstructure. The first dose map, weighted by the first weight, and thesecond dose map, weighted by the second weight, can be superimposed togenerate the composite dose map.

Implementations of the current subject matter can include, but are notlimited to, methods consistent with the descriptions provided herein aswell as articles that comprise a tangibly embodied machine-readablemedium operable to cause one or more machines (e.g., computers, etc.) toresult in operations implementing one or more of the described features.Similarly, computer systems are also contemplated that may include oneor more processors and one or more memories coupled to the one or moreprocessors. A memory, which can include a computer-readable storagemedium, may include, encode, store, or the like, one or more programsthat cause one or more processors to perform one or more of theoperations described herein. Computer implemented methods consistentwith one or more implementations of the current subject matter can beimplemented by one or more data processors residing in a singlecomputing system or across multiple computing systems. Such multiplecomputing systems can be connected and can exchange data and/or commandsor other instructions or the like via one or more connections, includingbut not limited to a connection over a network (e.g., the internet, awireless wide area network, a local area network, a wide area network, awired network, or the like), via a direct connection between one or moreof the multiple computing systems, etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject matterare described for illustrative purposes in relation to particularimplementations, it should be readily understood that such features arenot intended to be limiting. The claims that follow this disclosure areintended to define the scope of the protected subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 is a simplified diagram illustrating a radiation therapy systemequipped with a radiation detector for measuring radiation exiting froma patient in accordance with certain aspects of the present disclosure.

FIG. 2 is a simplified diagram illustrating the radiation therapy systemrotated during patient treatment in accordance with certain aspects ofthe present disclosure.

FIG. 3 is a simplified diagram illustrating formation of a transit imagefrom execution of segments in a treatment plan in accordance withcertain aspects of the present disclosure.

FIG. 4 is a process flow diagram illustrating a method of verifying adelivered radiation dose in accordance with certain aspects of thepresent disclosure.

FIG. 5 is a simplified diagram illustrating conversion factorscalculated from a calibration image and a dose map and stored in amultidimensional conversion structure in accordance with certain aspectsof the present disclosure.

FIG. 6 is a simplified diagram illustrating superposition of images togenerate additional conversion factors in the multidimensionalconversion structure in accordance with certain aspects of the presentdisclosure.

FIG. 7 is a process flow diagram illustrating a method of verifying adelivered radiation dose in accordance with certain aspects of thepresent disclosure.

DETAILED DESCRIPTION

To effectively treat some patients with radiation, it can be necessaryto apply varying amounts of radiation to different portions of apatient's anatomy and at different angles and rates of radiationexposure. These treatments can occur over an extended period of time,minutes, hours, or even days with different treatment sessions. Todetermine if the proper amount of radiation was given to a patient, aradiation detector can be used that detects the radiation exiting apatient and creates an electronic signal or digital output proportionalto the amount of radiation received. Because the treatment conditionscan change from moment to moment, the detected radiation can beconsidered as a combination of smaller, approximately constant,applications of radiation. The measurements of the detected exitradiation into these treatment segments can be converted to a dose. Themeasured dose can be compared to the exit detector planned or expecteddose in order to evaluate the accuracy of the delivered treatment. Thecomparison can be done on a segment by segment basis, or for anintegrated dose measured by the radiation detector.

Among the benefits described herein, the current subject matter allowsan efficient and accurate method of determining a delivered radiationdose to the exit detector from a single integrated image (or dose map)formed from a superposition of individual detections that were generatedunder spatially and/or temporally varying conditions. For example, someradiation detectors do not have a means of extracting measurements withfine time-resolution. For example, some radiation detectors provide onlya single integrated measurement available after a procedure. Where suchlimitations are present, computing an accurate dose based on the blendedmeasurements can be inaccurate or extremely difficult. Theimplementations described herein address this technical problem, amongothers.

Some implementations described herein relate to the radiation detectorbeing an EPID device. In other implementations, the radiation detectorcan be any type of radiation detector that performs substantially thesame functions as an EPID. For example, the radiation detector could bea device that directly uses diodes without a phosphor. In yet otherimplementations, the radiation detector can include an array ofionization chambers or an array of individual scintillating elements. Itshould be noted that the detector elements of the exit detector need notlie in the same plane. For example, provided that the locations of eachdetecting element are known, these locations can be accounted for in thedisclosed method. Thus a detector comprised of multiple planes or even aloose collection of detecting elements randomly located behind thepatient can be used.

FIG. 1 is a simplified diagram illustrating a radiation therapy system110 equipped with a radiation detector 180 for measuring radiationexiting from a patient 130 in accordance with certain aspects of thepresent disclosure. The radiation therapy system 110 can include agantry 140 that can rotate about the patient 130. Inside gantry 140,there is a radiation source, for example, a linear accelerator (LINAC),cobalt 60 source, etc., that directs radiation toward patient 130 in theform of treatment beam 150. The treatment beam 150 can also includescanning beams, where a small beamlet is scanned over the area that isrequired to be treated. The treatment beam 150 may be shaped by acollimator 160, for example, a multi-leaf collimator (MLC), beforereaching patient 130. In the example of the collimator 160 being a MLC,the collimation may block part of treatment beam 150 by providing aseries of narrow gaps between opposite leaves of the MLC, which combineacross multiple leaf pairs to form a desired shape, typically similar tothe tumor that is being irradiated. The example of a multi-leafcollimator is used herein. However, the present disclosure contemplatesany type of collimation device.

The treatment of patient 130 can be controlled by a radiation therapycontrol system 170, which can include, for example, processors,computers, hardware, computer programs, etc., that control theadministration of a radiation treatment plan 196 for the patient 130.The radiation therapy control system 170 can control, for example,treatment beam 150, position of gantry 140, beam shape created bycollimators 160, etc.

Radiation treatment plans, as used herein, can include any type ofinformation about radiation delivery, such as a treatment plan, obtainedin any manner, for example, delivery log information, or anymeasurements or other data that can provide information about thepatient entrance fluence, etc. Typical radiation treatment plans furtherinvolve defining specific machine parameters at precise, and typicallyfine, time intervals to most closely deliver the specified dose ofradiation to the target volume in the patient 130. Common parametersused in radiation treatment plans can include, for example, treatmentbeam shape or energy, orientation of the gantry, collimator leafpositions, patient anatomy (CT) image orientation with respect to thetreatment beam, etc.

The patient 130 can rest on patient couch 132 during treatment. Afterradiation passes through patient 130 and, at times, patient couch 132,the radiation can impact radiation detector 180. In someimplementations, radiation detector 180 can be connected to gantry 140or otherwise made to rotate with gantry 140 or the radiation detector180 can be comprised of a multitude of stationary or moving detectorspositioned around the patient in a cylindrical or any other shape.

Radiation impacting the radiation detector 180 may be detected as apattern related to the transmission and absorption of the radiation bythe patient anatomy and/or tumor(s). In one type of detector, theradiation detector 180 may convert the incident radiation to otherwavelengths of light, via a phosphor layer in radiation detector 180.Light from the phosphor may then be detected by photosensors andconverted into an electrical signal, essentially creating a pixel mapfor the radiation incident on radiation detector 180. The electricalsignals from radiation detector 180 can be acquired by, for example,analog-to-digital convertors, digitizers, etc. to acquire, filter,analyze, store or otherwise process the acquired exit radiationmeasurement information.

As part of the monitoring and quality assurance of radiotherapytreatment, systems and diagnostics can be used to estimate the dosedelivered to the patient 130 and compare it to the goals specified bythe radiation treatment plan. Such systems or software can be integratedinto the radiotherapy control system or may be part of a separatequality assurance (QA) computer 190 (as shown in FIG. 1 ). While theembodiment described herein utilizes a separate quality assurancecomputer, the present disclosure contemplates the concepts disclosedherein being implemented within the radiation therapy system's controlsystem or any other related system. Though FIG. 1 shows the QA computer190 and the radiation therapy control system 170 as separate components,the functionality described herein can be performed on an integratedsystem or distributed across any number or type of hardware or softwarecomponents that enable the specified functions herein to be performed.

FIG. 2 is a simplified diagram illustrating the radiation therapy systemrotated during patient treatment in accordance with certain aspects ofthe present disclosure. The gantry 140 can rotate any number or portionsof transits about the patient and or patient couch 132 such that thetreatment beam 150 arrives at the patient at different angles while thedistance from the point of beam emission to the radiation detector 180can be constant. Due to the rotation of the gantry 140, the path of thetreatment beam 150 through the patient can change. The treatment plancan control the angle of the gantry 140 (shown as angle A in FIG. 2 ),the apertures formed by any collimators, beam energy parameters, or thelike.

During patient treatment, due to rotation of the gantry 140, thetreatment beam 150 interacts with different parts of the patientanatomy. This, combined with the corresponding changes to treatment beam150 output can create a varying intensity of radiation that is detectedby the radiation detector 180. Over the treatment process, theaccumulated signal due to each portion of the treatment plan can beconsidered as an integrated image (or transit image). As used herein,the term “transit image” can refer to any number or combination ofindividual images and/or measurements resulting from the detection ofradiation at the radiation detector 180 during execution of thetreatment plan. Additional details of the transit image are describedbelow in the discussion of FIG. 3 .

As described herein, in some implementations, verification of the dosereceived by the patient can be performed by comparing a predicted exitdose (based on the treatment plan) to a measured exit dose (based on thetransit image received from the radiation detector 180). This comparisoncan be performed at each pixel of the radiation detector 180 and forindividual segments of a patient treatment procedure. Further details ofthe verification process are described below.

Treatment plan information can be acquired from a radiation therapysystem. Treatment plan information can include, for example, positionsand/or angles of the gantry 140, energy of the treatment beam 150,collimator positions (including leaf positions in a multileafcollimator), treatment beam 150 on/off status, predicted target doseinformation at varying portions of the patient anatomy, acceptablelimits or error in received patient dose, or the like. The treatmentplan information can be acquired from a connected treatment computer orcan be loaded onto the radiation treatment control system.

Patient image data can be acquired by an imaging system and can includeimages of the patient anatomy, for example, tissue, bone, tumors,cavities, or the like. Imaging devices that can generate the patientimage data can include, for example, computed tomography systems,magnetic resonance imaging systems, x-ray systems, or any combinationthereof. The patient image data can be in the form of static images orvideo images. The patient image data can be two-dimensional (as insingle static image), two-dimensional plus time (as in a time sequenceof two-dimensional images), three-dimensional (as in a number of staticimages acquired at different locations or planes in a three-dimensionalspace), or four-dimensional (as in a series of three-dimensional imagestaken at different times). Patient image data can include informationabout the physical location of the images, for example, coordinates,angles, or other identifying information specifying an image plane orlocation of an image volume. Similarly, when the patient image data hasa temporal component, the patient image data can include timestamps orsequencing information that identifies either an absolute time betweenimages or a relative or ordering of the images. In some implementations,the patient image data can be acquired prior to patient treatment andloaded into any of the systems described herein. In otherimplementations, the patient image data can be acquired later and thefeatures relying on the patient image data can be performedpost-treatment.

FIG. 3 is a simplified diagram illustrating formation of a transit imagefrom execution of segments in a treatment plan in accordance withcertain aspects of the present disclosure. Transit image data can begenerated by a superposition of data received from the radiationdetector 180 during radiation therapy. The timeline 310 shows an exampleof a treatment plan that proceeds by providing movement of the gantry140, modification of collimator positions, variations in beam energy,etc. The treatment plan can be divided into segments 320, of varying (orconstant) length. Each segment of the treatment plan contributes to thetransit image that is ultimately generated from measurements at theradiation detector 180. Conceptually, this is illustrated in FIG. 3 bythe two columns of images. In the example of FIG. 3 , each row of imagescorresponds to different times during the treatment plan. The leftcolumn (segment contribution) represents the image contributiongenerated at the radiation detector 180 during segments A, B, and C. Theright column (transit image data) represents the cumulative orintegrated image at the radiation detector 180 after each of segments A,B, and C. The cumulative image corresponds to the transit image data,which is acquired over some or all of the patient treatment.

As a specific example, an EPID can measure an induced charge at a“pixel” (or other small detection area) of the EPID. The charge can beproportional to the amount of incident radiation at that pixel. The rateat which charge is acquired can vary with the application of thetreatment plan and the location of the patient anatomy relative to thetreatment beam 150. In this way, the transit image data can be the totalcharge stored on, or measured from, the radiation detector 180 over someor all of the treatment by the treatment beam 150. The transit imagedata (e.g. in terms of acquired charge) can be converted to anotherunit, such as a calibrated unit. The calibrated unit can represent theraw signal from the radiation detector 180 combined with a calibrationfactor to convert the physical quantity (e.g. charge) detected at apoint or pixel on the radiation detector 180 to an image (e.g. anintensity, color, or the like).

In some implementations, the transit image data can be in the form ofraw data (e.g. charge at the radiation detector 180) that has beenpost-processed and/or otherwise converted into viewable images. Transitimage data of this type can be in the form of video or cine imagesspanning the treatment segments. Any number of frames of video can becombined into segments, and correspondingly any number of segments ofvideo can be combined to form the transit image data.

Though the explanation of the transit image data provided above wasdescribed in terms of a superposition of images, the process can beginwith the acquisition of the transit image data (e.g. an integratedimage), with the separation into segments performed subsequent topatient treatment. Once all of the required data is acquired, theprocess of comparing the measured dose (e.g. determined from the transitimage data as described below) to a desired dose (e.g. based on thetreatment plan) can begin. As shown in FIG. 3 , the treatment plan canbe made up of a number of individual treatment steps where treatmentbeam 150 settings, patient position, gantry 140 angle, or the like, aresubstantially constant. In this way, each of the segments can correspondto a time window where the delivery of dose to a portion of a patientanatomy is substantially constant (e.g. not changing or changing only toa small and acceptable amount). In some implementations, a segment canbe, for example, more than 1 second, 1 second, 0.5 seconds, 0.1 seconds,0.05 seconds, 0.01 seconds, less than 1 second, less than 0.5 seconds,less than 0.1 seconds, or the like. In other implementations, thesegments that make up a patient treatment need not be uniform in time.For example, during patient treatment, if the treatment beam 150 is onand the gantry 140 is in one particular spot for some relatively longspan of time, this can correspond to a first segment spanning that longspan of time. Then, if the gantry 140 rotates to another position andthe treatment beam 150 is on for a short period of time, this cancorrespond to a second segment spanning that shorter length of time. Inthis way, the segments can vary in duration to capture a sequence ofperiods of substantially constant patient treatment. By dividing thetreatment plan information according to these segments, each segment ofthe treatment plan information can also correspond to a constant flux ofradiation to the radiation detector 180. Subsequently, the measuredresponse (e.g. acquired charge) at the radiation detector 180 can beapproximately linear in time over the span of the segment.

A prediction of what should be measured at the radiation detector 180during a segment can be made. Predicted segment image data can bedetermined utilizing a predicted image calculation algorithm and atleast the patient image data and the treatment plan information. Thepredicted segment image data can be, for example, a portion(corresponding to a segment) of the transit image that would result fromthe radiation treatment plan when the radiation treatment plan isadministered as expected.

Under these conditions, knowing information about the state of thetreatment beam 150, the position of the gantry 140, the anatomy of thepatient (from the patient image data), and a calibration of theradiation therapy system 110 and the radiation detector 180, a predictedsignal or measurement at the radiation detector 180 can be determined.For example, the output of the radiation therapy system 110 can beexpressed in a radiation flux. The absorption or scattering of radiationby the patient anatomy, patient couch 132, and any other interveningmaterials, can be estimated based on measurements received at theradiation detector 180 during a calibration. Knowing the output of theradiation therapy system 110 specified in the treatment plan, this canbe converted with the calibration to calculate the predicted segmentimage data. This can also involve taking into account the time durationof the particular segment. In this way, the predicted segment image datacan be the product of the radiation flux reaching the radiation detector180 and the calibration, summed over the duration of the segment.

Because some radiation detectors, EPIDs for example, have a large numberof small detector surfaces (previously referred to as “pixels,”) themeasurements and predictions described herein can be performed on apixel-by-pixel basis. Among other reasons, this is due to the fact thatthe treatment beam 150 (and the patient anatomy) may not be uniform whenprojected onto the surface of the radiation detector 180. Accordingly,many of the implementations described herein require their owncalibration of the radiation detector 180 at each pixel and knowledge ofthe intervening patient anatomy and the angle and energy of thetreatment beam 150 corresponding to that particular pixel.

In other implementations, the predicted segment image data can begenerated by through use of a machine-learning program in which theelement-by-element dose response is derived from a collection of priorpatient or phantom measurements for which the treatment-time anatomy andthus detector dose was well-known. In other implementations, thismachine-learning approach can be combined with the above-describedapproach using the measured calibration data by informing theimplementation in which only measured calibration data is used.

In other implementations, a predicted segment image data can begenerated by converting a predicted dose (for example from a radiationtreatment plan, via Monte-Carlo simulation or any other computationalalgorithm capable of simulating the radiation behaviors of thedetector's constituent components). The predicted dose can then resultin a predicted signal contribution generated at the radiation detector180 for a given segment directly or using the appropriate calibrationfactors for each segment.

In yet another implementation, direct Monte-Carlo simulation using thetreatment plan and patient image data can be used to directly simulatethe response of the detector to the patient exit radiation in order tocalculate segment predicted images.

In yet another implementation, a measurement of the radiation fluencefrom detectors other than the EPID (e.g. near the radiation source,positioned at or around the radiation therapy system 110, etc.), or acalculation of the radiation fluence received by EPID pixels, can beconverted into the predicted segment image data. For example, a signalfrom a radiation detector near the radiation source can be combined withthe known collimator configuration, and patient image data (describinglocations, orientations, and materials of the patient anatomyinteracting with the treatment beam 150), to generate the predictedsegment image data without reliance on measurement from the EPID.

In a further implementation, the predicted segment image data can begenerated prior or after the generation of the transit image data. Forexample, predicted segment image data can be recalled from a databasewhere the conversion of the transit data to dose has been performed.When the recalled predicted segment image data represents a successfuloperation of the treatment plan, the predicted segment image data can beused to assemble the predicted integrated image, without having tore-compute the predicted integrated image from the radiation treatmentplan as described below. In another implementation, it is also possibleto use measured segment-by-segment images of the patient, measuredduring another treatment session, as the predicted segment-by-segmentimage. Digitally reconstructed radiographs, or measured radiographs ofthe patient, can also be used instead of the predicted image.

A predicted integrated image can be determined through superposition ofdata and/or images from the predicted segment image data. In someimplementations, the predicted integrated image can be the transit imagethat would be result from the treatment plan going as expected. Togenerate the predicted integrated image, one or more segments of thepredicted segment image data can be combined. The combination can be,for example, of a scalar output of the radiation detector 180 (e.g. ameasured current).

In other implementations, the predicted integrated image can be adigital superposition of a sequence of images generated from thepredicted segment image data (e.g. when the predicted segment image datahas been converted to a map or other visual image corresponding to asnapshot of the expected measurement at the radiation detector 180). Inyet other implementations, the predicted integrated image can bereplaced by a calculated cumulative dose to the radiation detector bythe radiation exiting the patient. Here, the calculated cumulative doseto the radiation detector 180 can be converted to an integratedpredicted image based on a known conversion factor, Monte Carlosimulations, prior patient data (e.g. known received doses from previoustreatments under the same conditions), or the like.

As described above, the predicted segment image data can be determinedon a pixel-by-pixel basis. Therefore, the predicted integrated imagecorresponding to the surface of the radiation detector 180 can begenerated by summing the contributions, for each pixel, over all thesegments. The pixels can then be arranged into the predicted integratedimage based on the locations of the pixels on the radiation detector180.

Measured segment responses can be determined from the transit image datautilizing the predicted segment image data and the predicted integratedimage. The output (e.g. current, intensity, etc.) of the radiationdetector 180 during a segment is the measured segment response. Themeasured segment response is similar to the predicted segment response,but refers to an actual measurement and not to an a priori calculatedvalue as with the predicted segment response. In other words, thetransit image data can be separated into measured segment responsesaccording to the various contributions estimated from the predictedsegment image data.

Because the predicted integrated image can be assembled from thesuperposition of the predicted segment image data, the fractionalportion or ratio of the contribution of a predicted segment image datato the predicted integrated image can be estimated for each segment. Thecontribution can be referred to as the predicted segment responsecontribution.

For example, based on the treatment plan, it could be determined thatthe predicted segment response contribution for a specific area or pixelof the exit detector of a first segment was 1% of an expected transitimage and the predicted segment response contribution of a secondsegment was 3% of the expected transit image. The predicted segmentresponse contribution can then be multiplied by the transit image datato generate the measured segment response. This segmentation of thetransit image data can provide benefits in dose verification sinceconversion to dose utilizes calibration factors that are dependent uponthe state of the treatment delivery for each segment. The effectivefield size and distance the patient is from the exit detector are twoexamples of parameters that can describe the state of treatmentdelivery.

The measured segment responses can be converted to measured segmentdoses (the dose that the patient or detector received during a segment).This can be done by, for example, accessing, from a database, conversionfactors based on the patient image data and a physical configuration ofthe radiation therapy system 110. The conversion factors can begenerated based on a calibration procedure such as described in thesubsequent section, in which the conversion factors depend upon, forexample, the impact of the patient image data (or patient anatomy) onthe radiation beam, the physical configuration (of the radiation therapysystem) of the corresponding segment, and how close the patient is tothe exit detector. The conversion factor can be applied to the measuredsegment responses to generate the measured segment doses. Also, themeasured segment doses can be directly compared to calculated expectedsegment doses, if available, in order to provide additionalverification.

In another implementation, the conversion factors can be modeled byanalytical functions, and the analytical functions fitted to thesecalibration factors can be used instead of the conversion factors fromthe calibration table.

In yet another implementation, an artificial neural network can betrained that receives the aforementioned inputs used to determine theconversion factor, and provides the conversion factors from acalibration table as the output. Any other computational fittingalgorithm could also be used in the same fashion.

The conversion factor can be a combination of one or more conversionfactors that are based on machine status, image data, patient data, orthe like. For example, based on the treatment plan and/or treatmentlogs, the input fluence, exit fluence, field size, beam energy,spectrum, and the like can be determined for each delivery segment and acalibration factor can be determined that converts a measured segmentresponse of the transit image to a measured segment dose.

The treatment plan and/or treatment logs can be acquired or accessedfrom a treatment planning system or other connected computer. Thetreatment plan can describe portions of the patient anatomy and specifya target dose, acceptable dose ranges, times of treatment, radiationtherapy system machine settings, a sequence of settings to beimplemented during treatment, or the like. The treatment logs caninclude similar information as the treatment plan, with one differencebeing that the values contained in the treatment logs can be the actualvalues that the radiation therapy system 110 implemented during theactual delivery or the like.

The input fluence, the amount of radiation exiting the collimationsystems prior to reaching the patient, can be determined from thetreatment plan, treatment log files, or radiation measuring device nearthe treatment beam 150 source or disposed in the treatment area. Theinput fluence can be determined by converting a signal generated by thearrival of radiation at the diagnostic to the input fluence based on thecalibration applied to the signal.

The exit fluence can include the energy spectrum or shape of theradiation pattern and direction of travel of the radiation wavesreceived at the radiation detector 180. Attenuation and/or scatteringfrom the patient and any other intervening physical structures canreduce the radiation reaching the radiation detector 180. The exitfluence can be considered as the input fluence minus theattenuation/scattering. Because of scattering or other distortions, theexit fluence pattern at the radiation detector 150 may not be the sameas the radiation pattern that would occur in the absence of interveningmaterial.

The field size can be based on a combination of collimator settings(either or both of multileaf collimators or collimating jaws), modeledestimates of the treatment beam 150 shape, or radiation scattered orleaked through the collimators during patient treatment. The beam energyspectrum can be determined based on radiation therapy system settings,measured or analytical spectra of the beam energy at given settings andmean beam energies, or the like.

The conversion factor can be a combination of a first conversion factor,a second conversion factor, and so on. In some implementations, thefirst conversion factor can include a number of independent basisparameters with a known relationship to a received dose. For example, afirst conversion factor can include a factor based on field size, afactor based on radiological path length, an output factor (e.g. adimensionless parameter describing a change in the output of theradiation therapy system 110 versus a change in the field size),electron density of the patient in between the source and the EPID, thepatient exit distance to the detector, input fluence, exit fluence, orthe like. Again, these factors can be independent from each other. Incontrast, there can be a second conversion factor that is an amalgamthat accounts for all remaining aspects of the system when converting ameasured image to a measured dose. The second conversion factor caninclude a numerical scaling, factors based on the treatment plan and/ortreatment logs, patient image data, or the like. Any of the conversionfactors can be in terms of other parameters such as attenuation,electron density, equivalent water density, fluence received at theradiation detector 180, or the like.

In some implementations, the conversion factor can be based on datareceived from an effective field size calculator, a ray traceralgorithm, or an exit fluence calculation algorithm. The effective fieldsize calculator can be, for example, the output of a Monte Carloalgorithm or other simulation module that takes into account beamparameters as well as any intervening collimation. The effective fieldsize calculator also can be replaced by an output factor calculatorwhich uses the same input parameters and provides a different outputparameter that is in close relation with the effective fieldsize/effective field width. The ray tracer algorithm can also utilize amodel of a radiation source that provides an estimate of thetrajectories of beamlets emitted from the source, to the patient, and tothe radiation detector 180 as well as patient anatomical images oranatomical structure outlines with density overrides. The ray traceralgorithm can include modeling of a linear accelerator, or a pointsource, or a finite sized radiation source. The exit fluence calculationalgorithm can process any combination of information from the patientanatomy data, radiation detector measurements, and the treatment plan togenerate data or images representing the exit fluence.

Any of the conversion factors can be accessed from a computer memory ora database where the conversion factors are stored. The conversionfactors can be in the form of a table, array, or representation of ananalytical expression defining the conversion factor (e.g. equationcoefficients). Similarly, any of the image data, radiation detectormeasurements, segment data, or the like, can be stored as tables,arrays, memory objects, image files, video, and the like. Any number ofparameters can be combined from any number of conversion factors toconvert the measured segment response to a measured segment dose. Forexample, the measured segment response can be a dimensionless number orsome other unit. Upon applying the conversion factor, the measuredsegment response can be in terms of, for example, Gy or rads.

In some implementations, the conversion factor can be determined byimplementation of a Monte Carlo method that takes into accountvariations in scattering and absorption of radiation by materialspresent along the radiation path. The materials can be determined fromthe radiation therapy system 110 setup (e.g. patient couch 132dimensions, air scattering) and also from the patient image data.

In other implementations, an inverse process can be performed wherepredicted transit image data can be generated. Here, beginning with apredicted segment dose or a predicted integrated dose, these quantitiescan be multiplied by the inverse of the corresponding conversionfactors. This can generate predicted segment image data or predictedtransit image data, respectively.

In another implementation, the measured segment responses along with thecorresponding conversion factor of each segment can be used as inputs toa machine-learning program or neural network that generates the measuredsegment dose. For example, a large variety of samples of the weightinggiven to different measured segment responses along with thecorresponding conversion factor of each segment and the correspondingsegment dose that provides the simple output results can be provided inthe training stage of the neural network. The training set could begenerated from a database of multiple patients, phantoms, or of the samepatient getting the treatment. Once the training is successfullyperformed, the artificial neural network can provide an optimizedmeasured segment dose.

In another implementation, the predicted segment response contributioncan be determined with a machine-learning program or neural network aswell. Such artificial neural networks can receive the relevant persegment parameters that affect the conversion factor of each segment asthe input. The output of such a neural network can be the segmentresponse contribution in percent or any other unit. The sample trainingdata for such a neural network could also be provided from a single ormultiple patient's treatment. The sample training data for a neuralnetwork can also come from plans specifically designed for suchtraining, or from a calibration process on phantoms. In any of theneural network processes described herein, the weights can be applied atan input layer or hidden layer when generating the desired output.

In another implementation it is also possible to combine two artificialneural networks from above in a fashion that the outputs from the latter(e.g. based on training/calibration plans) becomes the input to theformer (e.g. based on patient treatment data). Therefore, it is possibleto make the first artificial neural network mentioned above a hiddenlayer in the combined artificial neural network. In anotherimplementation, the inputs to both of the neural networks above, exceptfor the segment weights, can be the input to a larger artificial neuralnetwork that can provide the final cumulative dose as the output usingthe same training datasets mentioned above. In one implementation, theartificial neural network can perform this process for all pixels atonce. In another implementation, different artificial neural networkscould be trained for different pixels or different groups of pixels.

A measured dose map that includes a sum of the measured segment doses inthe radiation detector 150 can be compared to a planned dose map for theradiation detector 150 to assess radiation treatment delivery. Theplanned dose map can be based on the treatment plan information. Beforepatient treatment, the planned dose at a given point in the patientanatomy is determined during the planning process. This determinationcan drive optimization of the treatment plan and set the parameters usedby the radiation treatment device. After the measured segment dose isdetermined, the measured dose map (or individual measured segment doses)can be compared to planned dose map specifying a desired sum of doses(e.g. generated from the treatment plan) to determine if the treatmentwent as planned. In some implementations, when a difference is detectedbetween the measured dose map and the planned dose map, at one or morepixels in the radiation detector 180, a warning, report, or the like canbe provided. The providing can be in the form of generating anddisplaying a report including a comparison of a measured dose map to aplanned dose map. In other implementations, the providing can be in theform of generating a warning based on the difference of the measureddose map to the planned dose map. The warning can be further based onthe measured dose being outside of a predetermined dose limit. Thewarning can be text or images generated and displayed at a computingdevice.

In other implementations, if one or more points in the dose differencemap exceed a preset user-defined tolerance, the radiation therapy system110 can be interrupted during delivery of radiation. This interruptioncan include transmitting a command to a control system to cause, forexample, cessation of emission from the radiation beam source, closingof a collimator (e.g. jaws or an MLC), or the like, to guard againstpotential misadministration and allow clinical personnel adequate timeto determine why the specified tolerance was exceeded.

FIG. 4 is a process flow diagram illustrating a method of verifying adelivered radiation dose in accordance with certain aspects of thepresent disclosure.

At 405, treatment plan information can be acquired from a radiationtherapy system 110.

At 410, patient image data can be acquired.

At 415, transit image data received from an electronic portal imagingdevice during radiation therapy can be acquired.

At 420, treatment plan information can be divided into a number ofsegments.

At 425, predicted segment image data can be determined utilizing apredicted image calculation algorithm and at least the patient imagedata and the treatment plan information.

At 430, a radiation detector response calibration can be determinedutilizing the radiation therapy plan, exit radiation measurementinformation, and patient anatomy information.

At 435, measured segment responses can be determined from the transitimage data utilizing the predicted segment image data and the predictedintegrated image.

At 440, the measured segment responses can be converted to measuredsegment doses.

At 445, a measured dose map comprising a sum of the measured segmentdoses can be compared to a planned dose map based on the treatment planinformation to assess radiation treatment delivery.

FIG. 5 is a simplified diagram illustrating conversion factorscalculated from a calibration image 510 and a dose map 520 and stored ina multidimensional conversion structure 530 in accordance with certainaspects of the present disclosure.

Radiation detectors can be calibrated to provide a conversion factorbetween a measured response and an actual dose delivered to a patient orcalibration object. In some cases, a limited set of conversion factorscan be available based on a finite number of radiation therapy systemconfigurations. In these cases, there can be a need to determineadditional conversion factors for radiation therapy systemconfigurations that were not used in a previous calibration. To providethe additional conversion factors, available calibration images can beweighted and combined to generate composite calibration imagesrepresentative of the desired radiation therapy system configurations.Similarly, a dose calculation engine can provide an estimate of the dosedelivered under the desired system settings. The estimated dose and thecomposite calibration images can be used to generate the desiredconversion factors.

As described herein, the response of the radiation detector can beaffected by a number of factors. These factors can include, for example,the pixel position on the radiation detector, an effective field size oroutput factor, whether the incoming radiation is primary (e.g. directlyfrom the source and/or through a patient) or scattered (e.g. radiationscattered from the patient or other object that eventually reaches theradiation detector, patient exit distance to the radiation detector 180,radiological path length, and the like. A conversion factor can convertthe response of the radiation detector (e.g. in the form of acalibration image) to a dose.

Each factor that affects the response of the radiation detector can actas a basis parameter for a conversion factor. The basis parameters inmany cases can be independent, but it is contemplated that some basisparameters can be interrelated. Assuming independent basis parameters, aconversion factor can be expressed asC=f(x ₁ ,x ₂ ,x ₃, . . . )  (1)

Any number or combination of basis parameters can be used to determinethe conversion factor. In one example, x₁ can be a pixel position, x₂can be an effective field size, x₃ can be a primary signal ratio (PSR)(e.g. a fraction of time in the treatment plan when the pixel is in thedirect path of the radiation beam 150), patient exit distance to thedetector, radiological path length, patient exit fluence, or the like.

In one implementation, the response of the radiation detector 180 can bebased on the primary signal ratio. Based on the system geometry and theradiation treatment plan, it can be determined whether a pixel of theradiation detector 180 is being exposed to primary radiation. Forexample, if a pixel (e.g. a center pixel) is in the radiation beam pathduring the entire treatment plan (e.g. not blocked by any collimation),then the primary signal ratio for that pixel would be 1.0. Similarly, apixel that was exposed to the radiation beam 150 for 75% of thetreatment plan would have a primary signal ratio of 0.75. It can beassumed, in this implementation, that the response of the radiationdetector is directly proportional to the primary signal ratio.

In other implementations, the response of the radiation detector 180 canbe based on a ratio of the actual amount of primary radiation reachingthe radiation detector 180 to the actual amount of secondary radiationreaching the radiation detector 180. With the primary signal ratio, itcan be assumed, in some implementations, that the signal is fullstrength when the pixel is in the direct path radiation beam 150 andzero when it is not. A more complicated representation can includedetermining the contribution of secondary radiation based on thetreatment plan and the system geometry. For example, all pixels can bereceiving some amount of secondary radiation due to scattering, beamedges, or the like. The total radiation at a pixel can then be the sumof the primary radiation can be the sum secondary radiation. Differentdose calculation engines or Monte Carlo simulations can also be used fordetermining the primary and secondary radiation contribution to eachpixel or groups of pixels.

Some radiation detectors, for example EPIDs, can have a differentresponse to primary radiation (e.g. along the direct path of theradiation beam 150) than to secondary radiation (e.g. received at thepixel through scattering). For example, a given amount of primaryradiation can generate a primary dose measured at the EPID. Similarly,the same amount of secondary radiation can generate a secondary dose atthe EPID. Because of the difference in the EPID response between the tworadiation types, the primary dose can be different than the secondarydose. In some implementations, the primary dose can be expressed asα (EPID response primary)=Primary Dose  (2)Similarly, the secondary dose can be expressed asβ (EPID response secondary)=Secondary Dose.  (3)

In Eqs. 2 and 3, α and β are EPID response coefficients for thedifferent types of radiation.

In both implementations above, the measured dose at the radiationdetector is linear with the measured response. Accordingly, a linear fitcan be applied to the calculated dose versus response data. The slope ofthe linear fit can be stored as the conversion factor corresponding to aparticular set of basis parameters (e.g. pixel position and field size).It should be noted that even though the relationship between theresponse and dose can seem like a linear correlation, the α and β andcan be very complex functions of other parameters that affect thecalibration such as field size, radiological path length, beam energy,fluence mode, detector position, patient exit distance to the radiationdetector 150, etc. In the simplified example presented in FIGS. 5 and 6, where the patient is not present, α and β and are only dependent onthe pixel position, PSR and field size. In more complex cases, when apatient or attenuator is present in the beam, there can be one such 3Dmatrix for each amount of attenuation and patient exit distance todetector. In such a case, it is also possible to store the exact sameinformation in a 1.5 dimensional array. It is also possible to furtherrefine these conversion factors by introducing more factors that couldaffect conversion factors. There can be more dimensions added to thismultidimensional array of conversion factors as more conversionaffecting parameters are taken into account.

A multidimensional conversion structure 530 can be generated to allowstorage and access of the conversion factors α and β and or anyparameter that is a more complex function of both parameters along withany other aforementioned parameters such as PSR and field size, amongothers. The multidimensional conversion structure 530 can be amultidimensional array, a matrix, collection of tables, objects or codemodules, or the like. As such, the multidimensional conversion structure530, stored on a server or other computer device or computer memory, canbe populated with conversion factors that relate a calibration image 510to a dose map 520. The conversion factors can include a first conversionfactor calculated, based on a first radiation therapy systemconfiguration, to convert a first calibration image (e.g. characterizingthe EPID response) from a radiation detector (e.g. the EPID) to a firstdose map 520. A radiation therapy system configuration (and basisparameters) can include any combination of, for example, the pixelposition, effective field size, PSR, beam energy, beam fluence mode,collimator settings, exit length, or the like.

In the example illustrated by FIG. 5 , the multidimensional conversionstructure 530 is shown conceptually as a cubic structure with eachdimension corresponding to a basis parameter (e.g. effective field size,pixel position, and PSR). The conversion factor corresponding to a PSRof 1.0 (100% primary radiation) at a particular field size and pixelposition is indicated by the star.

In some implementations, the calibration image 510 can be stored as atwo dimensional image with each pixel or coordinate of the calibrationimage 510 having an associated intensity or value. The intensity orvalue of the calibration image 510 can represent the measured responseof the radiation detector acquired during a calibration. Similarly,using a dose calculation engine, a dose map 520 can be generated withdose values that correspond to a predicted dose based on the settingspresent during the calibration. In the example illustrated by FIG. 5 ,the measured values of the calibration image are represented bydifferent colors. Either on a pixel by pixel basis, or using entireimages (e.g. 2-D, 3-D, etc.), the conversion factors can be determinedby dividing the values comprising the dose map 520 by the valuescomprising the calibrated image. As used herein, the terms “map” and“image” can refer to a single element or value, a one-dimensional arrayor list of values, a two-dimensional array or table, up to an arbitrarydimensionality of the input data (e.g. dose values and/or calibrationimages).

Also, as illustrated in the example illustrated by FIG. 5 , the pixelpositions can be expressed in terms of ordered pairs (e.g. X and Ycoordinates on a surface of the radiation detector). In this way, thepixel positions can be expressed as a basis parameter even though theyrefer to points on a two-dimensional surface.

The multidimensional conversion structure 530 can also include a secondconversion factor converting, based on a second radiation therapy systemconfiguration, a second calibration image from the radiation detector toa second dose map 520.

As mentioned above, there can be a request for a third parameteraffecting the conversion factors, for example, the Primary Signal Ratio(PSR). The PSR is always 100% when comparing a single calibrationresponse image and its corresponding predicted dose map. In the nextsection, we introduce a novel method that can use a limited number ofindividual calibration images for the purpose of finding the conversionfactors, for example, for a set of PSRs (other than 100%), a set ofradiological path lengths other than the ones measured, effective fieldsizes other than the ones measured, exit distances other than the onesmeasured, or other parameters that affect the conversion factors andhave not been determined yet. The request can be made to the server froma requesting device, or from another program operating on the server.

FIG. 6 is a simplified diagram illustrating superposition of images togenerate additional conversion factors in the multidimensionalconversion structure 530 in accordance with certain aspects of thepresent disclosure.

As discussed above, is not always possible to perform a separateradiation delivery and a dose calculation in order to collect thecorrect measured calibration image and predicted dose map fordetermining every conversion factor in the multidimensional conversionstructure 530. This is because the number of required measurements anddose calculations can be extremely large, due to the largedimensionality of the multidimensional conversion structure 530. Inorder to achieve the calculation of the conversion factors foradditional (or every) cell in the multidimensional conversion structure530, two or more dose maps can be combined with the correspondingcalibration images with different weights in order to provide thedesired conversion factor (e.g. another conversion factor). Use ofdifferent weights guarantees that we find conversion factors, forexample, for all possible PSRs, radiological path lengths, exitdistances to the radiation detector 180, effective field sizes, oroutput factors. For example, in FIG. 6 , any point inside the verticalband in Image B that is not in the vertical band of Image A has a PSR of5%, while all the pixels that are in the vertical band of A and B have aPSR of 100%.

In some implementations, a composite calibration image 630 can begenerated at the server by performing the following the followingoperations. A first weight can be applied to the first calibration image610, for example from an EPID, to generate a weighted first calibrationimage. A second weight can be applied to the second calibration image620, which can also be from an EPID, to generate a weighted secondcalibration image. Then, the weighted first calibration image can besuperimposed with the weighted second calibration image to generate thecomposite calibration image 630. Also, the dose calculation engine cangenerate a composite dose map based at least on a first dose map (suchas the calculated expected dose for calibration image A), a second dosemap (such as the calculated expected dose for calibration image B), thefirst weight, and the second weight. It is also possible to calculatethe corresponding parameters such as the effective field size,radiological path length, and patient exit distance to EPID for thiscombination of the two calibration images and/or dose maps.

One example of this process is illustrated in FIG. 6 . In this example,the request could be for a conversion factor corresponding to a PSR of0.05. Here, this conversion factor does not presently exist in themultidimensional conversion structure 530. However, a first calibrationimage 610 (calibration image A) corresponding to a PSR of 1.0 can becombined with a second calibration image 620 (calibration image B)corresponding to a PSR of 1.0. To generate a composite calibration image630 corresponding to a PSR of 0.05, the first calibration image 610 canbe weighted (e.g. multiplied) by 0.95 (e.g. a primary radiationfraction) and the second calibration image 620 can be weighted by 0.05(e.g. a secondary radiation fraction). The resultant compositecalibration image 630 can be the sum or superposition of the two images.The effective field size of the superposition result, for example, canbe calculated using a weighted average of the two field sizes combinedto create the superimposed image.

The process outlined above can also include generating, by a dosecalculation engine, a composite dose map 660 based on the first dose map640, the second dose map 650, the first weight, and the second weight.The dose calculation engine can be, for example, a code module, separatecomputer, that can generate a dose based on input parameters including,for example, calibration data (e.g. calibration images), the treatmentplan, or any combination thereof. Dose maps corresponding to theweightings can be also generated by inputting the unweighted calibrationimages (e.g. calibration image A and calibration image B) to the dosecalculation engine.

In the example shown in FIG. 6 , the dose calculation engine cangenerate a first conversion factor (converting the first calibrationimage 610 to a first dose map 640, e.g. dose map A) and a secondconversion factor (converting the second calibration image 620 to asecond dose map 650, e.g. dose map B). These dose maps can be weightedby the respective weightings that are applied to the calibration images.The composite dose map 660 can be generated by superimposing the firstdose map 640, weighted by the first weight, and the second dose map 650,weighted by the second weight. Referring back to the example shown inFIG. 6 , dose map A 640 can be weighted by 0.95 and dose map B 650 canbe weighted by 0.05 into a composite dose map 660 with a PSR of 0.95.

In some implementations, the server can generate conversion factors thatconvert images to dose maps. The conversion factors can correspond toradiation therapy system configurations that were not present or used ingenerating the calibration images. In one example implementation, theconversion factor can correspond to a first radiation therapy systemconfiguration that is different than a second radiation therapy system,such as one corresponding to at least one of the first calibration image610 or the second calibration image 620.

Accordingly, in some implementations, the conversion factor can begenerated that can convert the composite calibration image 630 into thecomposite dose map 660. For example, the conversion factor can resultfrom the composite dose map 660 divided by the composite calibrationimage 630 which was generated using a different set of weights ordifferent set of images, as shown by the equation in FIG. 6 . Theconversion factor can be stored in multidimensional conversion structure530. The conversion factor, generated from composite dose map 660 andcomposite calibration image 630, can be transmitted from the server tothe requesting device. As such, the implementations described hereinprovide a number of technical benefits, including allowing generation ofadditional conversion factors (shown as stars in FIG. 6 ) to bepopulated in the multidimensional conversion structure 530 that maycorrespond to radiation therapy system configurations not alreadyrepresented in the multidimensional conversion structure 530. Theadditional conversion factors, in the example shown in FIG. 6 , cancorrespond to a range of PSRs.

In other implementations, the conversion factor in multidimensionalconversion structure 530 can be based on, for example compositecalibration image 630 and composite dose map 660. In someimplementations, the conversion factor can be generated by thesuperposition of more than two calibration images and more than two dosemaps.

In some implementations, the server can be, for example, part of theradiation therapy system, a mainframe, a database, or the like. Therequesting device can be an internal component of the server, such asanother processor or requesting program. The requesting device can alsobe a client computer, for example, a computer used by quality assurancepersonnel or a clinical worker, or the like.

The examples given herein, in particular with reference to FIG. 5 andFIG. 6 generating composite images from different PSRs, are notlimiting. The method of generating composite images (and theircorresponding conversion factors) can be based on any of the basisparameters. For example, the above method can be used to generate aconversion factor corresponding to a missing effective field sizemeasurement. Similarly, the method can be applied to generate aconversion factor corresponding to, for example, a pixel position,patient exit distance to the radiation detector 180, radiological pathlengths, or other parameters missing in the multidimensional conversionstructure 530. Other parameters can include, for example, leakagethrough a collimator (MLC or jaws), an average exit distance, a localexit distance, a product of the local exit distance and the radiologicalpath length, or the like. The local exit distance can be the distancefrom the patient to the detector. Due to the shape and contours of apatient's body, this can be different than a constant distance such asthe surface of a patient couch to a detector surface. The example givenwith the PSR is only one example and should not be considered as alimiting or exclusionary feature. In this way, any combination of theparameters can be used to generate the conversion factors.

Also, the conversion factors generated according to the above methodscan be implemented when determining dose due to segment contributions oftransit image data. For example, a segment of the treatment plan may nothave a needed conversion factor for the dose contribution for thesegment. The above method can be used to provide the needed predicteddose contribution. The predicted dose contribution can be converted tothe corresponding predicted segment image data with the calibrationfactor. The predicted segment image data can be superimposed to generatethe predicted integrated image, as described above.

In some implementations, the calibration process described above couldbe performed in a model development stage to populate the initialconversion factors and the conversion table could be adjusted for auser's treatment delivery machine based on a different method using alimited number of measurements.

Generating additional conversion factors can provide a more detailedmultidimensional conversion structure 530, and hence a more robustcalibration. Generating the dose maps and corresponding measureddetector images to cover all possible calibration affecting parameterssuch as field size, PSR, radiological path length, patient exit distanceto detector and can require a significant amount of dose calculationtime as well as measurement time on the treatment delivery machine. Toput it in perspective, if we only perform the calibration dosecalculation and measurements for 10 field sizes, 10 different PSRs and10 different radiological path lengths (10 different thickness ofmaterial, and 10 different patient exit distances for 10 differentpixels, we would require to perform the dose calculations andmeasurements for 105 (10×10×10×10×10) different configurations that isextremely difficult to perform. However, using the methodology explainedin this document, it is possible to perform a calibration with similaraccuracy, only by performing as low as 50 (10+10+10+10+10) measurements.The numbers used here should not be limiting and it is clearly possibleto further reduce the number of calibration measurements mentioned here.Another benefit can be the improved memory use and processing time of acomputer tasked with providing conversion factors upon demand once theconversion factors are precalculated and the multidimensional array isrepopulated. For example, the multidimensional conversion structure 530can represent a sparse data set (i.e. one taking up less computermemory). However, with the above method, conversion factors can begenerated or provided on demand without having to significantly expandthe memory required for the multidimensional conversion structure 530.With a sparse multidimensional conversion structure 530, access time andfile size can be reduced. As a result, desired calibration points can begenerated in fewer processing cycles. Filling the multidimensionalconversion structure 530 with all desired points would be anapproximately O(N^(M)) operation, where N is an average number ofelements in a basis parameter and M is the dimensionality of themultidimensional conversion structure 530. This extremely expensiveoperation can be reduced to approximately O(AN*M), where A is an averagesparseness of the multidimensional conversion structure 530.Furthermore, because the desired conversion factor can be determined ondemand (and not necessarily stored in the multidimensional conversionstructure 530), any conversion factor can be determined while retainingan approximately constant memory allocation. As can be seen, thebenefits of the procedures described herein provide greater benefitswhen the number of basis parameters is larger (i.e. when the conversionfactor is more accurately represented). Also, any of the methodsdescribed herein can be implemented on a neural network or otherartificial intelligence framework to optimize the generation ofconversion factors that can be used in a dose verification process.

In one such implementation, an artificial neural network can be designedto receive the some or all of the calibration determining parameter asthe input. The output, in one implementation, can be the conversionfactor. The data generation process, using the superimposing procedureexplained above, can be used to generate a large training dataset thatprovides the sample input parameters. The sample outputparameter/parameters can be generated by dividing the compositecalibration dose by the composite measured calibration image. In someimplementation, a single artificial neural network with any arbitrarynumber of inputs or outputs can be created that receives the inputparameters for a single pixel or a group of pixels and yields theconversion factors, as well as other arbitrary outputs of a single pixelor a group of pixels. These trained artificial neural networks can alsobe a hidden layer in a larger artificial neural network capable ofproviding the conversion factors for all pixels of the detector at once.In other implementations, a single artificial neural network can betrained for a constant condition or parameter. For example, a singleparameter, such as field size, can be held constant. This singleparameter can be provided as input to the single artificial neuralnetwork, as well as providing the rest of the parameters affecting theconversion factor. In this way, the single artificial neural network canreceive the needed parameters, and either including or excluding theconstant factor. In such implementation, a specific neural networktrained for the corresponding conditions in each segment can be used forfinding the conversion factor of that particular segment. Multipleneural networks could be linked for each segment in such animplementation to generate the final conversion factor.

FIG. 7 is a process flow diagram illustrating a method of verifying adelivered radiation dose in accordance with certain aspects of thepresent disclosure.

At 710, based on the treatment plan, for each pixel a number ofeffective field sizes and PSR can be calculated or specified forinclusion in the multidimensional conversion structure 530.

At 720, based on the treatment plan, a dose calculation can be generatedwith a dose calculation engine. The dose calculation can be based on thedose received by a water phantom.

At 730, a two dimensional dose map can be extracted from the dosecalculation. The two dimensional dose map can correspond to the dose ata plane of the radiation detector.

At 740, for each effective field size and PSR, a conversion factor canbe calculated and stored in the multidimensional conversion structure530 for all pixel positions.

The present disclosure contemplates that the calculations disclosed inthe embodiments herein may be performed in a number of ways, applyingthe same concepts taught herein, and that such calculations areequivalent to the embodiments disclosed.

The present disclosure also contemplates that any of the conversionfactors calculated or determined herein can result from one or moremathematical operations, implemented by a physical processor. Theoperations can include, for example, addition, subtraction,multiplication, division, any combination thereof, applied to theimages, maps, parameters, or other conversion factors described herein.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” (or “computer readablemedium”) refers to any computer program product, apparatus and/ordevice, such as for example magnetic discs, optical disks, memory, andProgrammable Logic Devices (PLDs), used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” (or “computer readable signal”)refers to any signal used to provide machine instructions and/or data toa programmable processor. The machine-readable medium can store suchmachine instructions non-transitorily, such as for example as would anon-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it used, such a phrase is intendedto mean any of the listed elements or features individually or any ofthe recited elements or features in combination with any of the otherrecited elements or features. For example, the phrases “at least one ofA and B;” “one or more of A and B;” and “A and/or B” are each intendedto mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” Use of the term “based on,” above and in theclaims is intended to mean, “based at least in part on,” such that anunrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, computer programs and/or articles depending on thedesired configuration. Any methods or the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. The implementations set forth in the foregoing description donot represent all implementations consistent with the subject matterdescribed herein. Instead, they are merely some examples consistent withaspects related to the described subject matter. Although a fewvariations have been described in detail above, other modifications oradditions are possible. In particular, further features and/orvariations can be provided in addition to those set forth herein. Theimplementations described above can be directed to various combinationsand subcombinations of the disclosed features and/or combinations andsubcombinations of further features noted above. Furthermore, abovedescribed advantages are not intended to limit the application of anyissued claims to processes and structures accomplishing any or all ofthe advantages.

Additionally, section headings shall not limit or characterize theinvention(s) set out in any claims that may issue from this disclosure.Specifically, and by way of example, although the headings refer to a“Technical Field,” such claims should not be limited by the languagechosen under this heading to describe the so-called technical field.Further, the description of a technology in the “Background” is not tobe construed as an admission that technology is prior art to anyinvention(s) in this disclosure. Neither is the “Summary” to beconsidered as a characterization of the invention(s) set forth in issuedclaims. Furthermore, any reference to this disclosure in general or useof the word “invention” in the singular is not intended to imply anylimitation on the scope of the claims set forth below. Multipleinventions may be set forth according to the limitations of the multipleclaims issuing from this disclosure, and such claims accordingly definethe invention(s), and their equivalents, that are protected thereby.

What is claimed is:
 1. A method for implementation by at least one dataprocessor for radiation therapy treatment verification, the methodcomprising: acquiring treatment plan information from a radiationtherapy system; acquiring patient image data; acquiring transit imagedata received from a radiation detector during radiation therapy;dividing the treatment plan information into a plurality of segments;determining predicted segment image data utilizing a predicted imagecalculation algorithm and at least the patient image data, and thetreatment plan information; determining a predicted integrated imagethrough superposition of the predicted segment image data; determiningmeasured segment responses from the transit image data utilizing thepredicted segment image data and the predicted integrated image;converting the measured segment responses to measured segment doses; andcomparing a measured dose map comprising a sum of the measured segmentdoses to a planned dose map based on the treatment plan information toassess radiation treatment delivery.
 2. The method of claim 1, thecomparing further comprising: transmitting, to a recipient device, adifference between the measured dose map and the planned dose map. 3.The method of claim 2, the comparing further comprising at least one of:displaying, at an electronic display, a report comprising thedifference; and generating, at an electronic device, a warning based onthe difference.
 4. The method of claim 1, the converting comprisingutilization of an effective field size calculator.
 5. The method ofclaim 1, the converting comprising utilization of a ray traceralgorithm.
 6. The method of claim 1, the converting comprising:accessing, from at least one database, a measurement of an output of atreatment beam, the patient image data, and a physical configuration ofthe radiation therapy system; generating a conversion factor based onthe accessed measurement, the patient image data, and the physicalconfiguration corresponding to at least one of the plurality ofsegments; and applying the conversion factor to the measured segmentresponses to generate the measured segment doses.
 7. The method of claim1, wherein the patient image data comprises three-dimensional images ofpatient anatomy.
 8. The method of claim 1, wherein the plurality ofsegments correspond to time windows where dose delivery is substantiallyconstant.
 9. The method of claim 1, the converting comprising: executinga neural network to generate the predicted integrated image by weightinga predicted segment response contribution as part of an input layer ofthe neural network.
 10. The method of claim 1, wherein the comparison ofthe measured dose map to the planned dose map comprises a comparison ofa first sum of the measured segment doses to a second sum of desireddoses.
 11. The method of claim 1, further comprising: generating anelectronic warning at a display device based on the comparing of themeasured dose map to the planned dose map, when a dose is outside of apredetermined dose limit.
 12. The method of claim 1, the determining ofthe measured segment responses comprising: extracting a predictedresponse contribution based on the predicted segment image data and thepredicted integrated image; and generating the measured segmentresponses from the predicted response contribution and the transit imagedata.
 13. A computer program product comprising a non-transitory,machine-readable medium storing instructions which, when executed by atleast one programmable processor, cause the at least one programmableprocessor to perform operations comprising: acquiring treatment planinformation from a radiation therapy system; acquiring patient imagedata; acquiring transit image data received from a radiation detectorduring radiation therapy; dividing the treatment plan information into aplurality of segments; determining predicted segment image datautilizing a predicted image calculation algorithm and at least thepatient image data, and the treatment plan information; determining apredicted integrated image through superposition of the predictedsegment image data; determining measured segment responses from thetransit image data utilizing the predicted segment image data and thepredicted integrated image; converting the measured segment responses tomeasured segment doses; and comparing the measured segment doses todesired doses to assess radiation treatment delivery.
 14. The computerprogram product of claim 13, the converting comprising: accessing, fromat least one database, a measurement of an output of a treatment beam,the patient image data, and a physical configuration of the radiationtherapy system; generating a conversion factor based on the accessedmeasurement, the patient image data, and the physical configurationcorresponding to at least one of the plurality of segments; and applyingthe conversion factor to the measured segment responses to generate themeasured segment doses.
 15. The computer program product of claim 13,the converting comprising: executing a neural network to generate thepredicted integrated image by weighting a predicted segment responsecontribution as part of an input layer of the neural network.
 16. Thecomputer program product of claim 13, the determining of the measuredsegment responses comprising: extracting a predicted responsecontribution based on the predicted segment image data and the predictedintegrated image; and generating the measured segment responses from thepredicted response contribution and the transit image data.
 17. A systemcomprising: a radiation therapy system comprising: a radiation detector;and a radiation source configured to generate a treatment beam thatintersects the radiation detector; at least one programmable processor;and a non-transitory machine-readable medium storing instructions which,when executed by the at least one programmable processor, cause the atleast one programmable processor to perform operations comprising:acquiring treatment plan information from the radiation therapy system;acquiring patient image data; acquiring transit image data received fromthe radiation detector during radiation therapy; dividing the treatmentplan information into a plurality of segments; determining predictedsegment image data utilizing a predicted image calculation algorithm andat least the patient image data, and the treatment plan information;determining a predicted integrated image through superposition of thepredicted segment image data; determining measured segment responsesfrom the transit image data utilizing the predicted segment image dataand the predicted integrated image; converting the measured segmentresponses to measured segment doses; and comparing the measured segmentdoses to desired doses to assess radiation treatment delivery.
 18. Thesystem of claim 17, the converting comprising: accessing, from at leastone database, a measurement of an output of the treatment beam, thepatient image data, and a physical configuration of the radiationtherapy system; generating a conversion factor based on the accessedmeasurement, the patient image data, and the physical configurationcorresponding to at least one of the plurality of segments; and applyingthe conversion factor to the measured segment responses to generate themeasured segment doses.
 19. The system of claim 17, the convertingcomprising: executing a neural network to generate the predictedintegrated image by weighting a predicted segment response contributionas part of an input layer of the neural network.
 20. The system of claim17, the determining of the measured segment responses comprising:extracting a predicted response contribution based on the predictedsegment image data and the predicted integrated image; and generatingthe measured segment responses from the predicted response contributionand the transit image data.